'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    b.T
    __lowerCamelCase            =    np.sum(np.square(UpperCamelCase__							)							,    axis=1							)
    __lowerCamelCase            =    np.sum(np.square(UpperCamelCase__							)							,    axis=0							)
    __lowerCamelCase            =    np.matmul(UpperCamelCase__							,    UpperCamelCase__							)
    __lowerCamelCase            =    aa[:, None] - 2 * ab + ba[None, :]
    return d






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Tuple:
    __lowerCamelCase            =    x.reshape(-1							,    3							)
    __lowerCamelCase            =    squared_euclidean_distance(UpperCamelCase__							,    UpperCamelCase__							)
    return np.argmin(UpperCamelCase__							,    axis=1							)




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	str				=["pixel_values"]






     def __init__(					self						:						Union[str, Any]    ,							a						:						Optional[Union[List[List[int]], np.ndarray]] = None    ,							a						:						bool = True    ,							a						:						Dict[str, int] = None    ,							a						:						PILImageResampling = PILImageResampling.BILINEAR    ,							a						:						bool = True    ,							a						:						bool = True    ,							**a						:						Optional[int]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    size if size is not None else {'''height''': 2_56, '''width''': 2_56}
         __lowerCamelCase            =    get_size_dict(a       )
         __lowerCamelCase            =    np.array(a       ) if clusters is not None else None
         __lowerCamelCase            =    do_resize
         __lowerCamelCase            =    size
         __lowerCamelCase            =    resample
         __lowerCamelCase            =    do_normalize
         __lowerCamelCase            =    do_color_quantize






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						np.ndarray    ,							a						:						Dict[str, int]    ,							a						:						PILImageResampling = PILImageResampling.BILINEAR    ,							a						:						Optional[Union[str, ChannelDimension]] = None    ,							**a						:						str    ,							):





         """simple docstring"""



         __lowerCamelCase            =    get_size_dict(a       )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""       )
         return resize(
             a    ,							size=(size['''height'''], size['''width'''])    ,							resample=a    ,							data_format=a    ,							**a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						np.ndarray    ,							a						:						Optional[Union[str, ChannelDimension]] = None    ,							):





         """simple docstring"""



         __lowerCamelCase            =    rescale(image=a    ,							scale=1 / 1_27.5    ,							data_format=a       )
         __lowerCamelCase            =    image - 1
         return image






     def   SCREAMING_SNAKE_CASE__  (					self						:						int    ,							a						:						ImageInput    ,							a						:						bool = None    ,							a						:						Dict[str, int] = None    ,							a						:						PILImageResampling = None    ,							a						:						bool = None    ,							a						:						Optional[bool] = None    ,							a						:						Optional[Union[List[List[int]], np.ndarray]] = None    ,							a						:						Optional[Union[str, TensorType]] = None    ,							a						:						Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST    ,							**a						:						Dict    ,							):





         """simple docstring"""



         __lowerCamelCase            =    do_resize if do_resize is not None else self.do_resize
         __lowerCamelCase            =    size if size is not None else self.size
         __lowerCamelCase            =    get_size_dict(a       )
         __lowerCamelCase            =    resample if resample is not None else self.resample
         __lowerCamelCase            =    do_normalize if do_normalize is not None else self.do_normalize
         __lowerCamelCase            =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         __lowerCamelCase            =    clusters if clusters is not None else self.clusters
         __lowerCamelCase            =    np.array(a       )

         __lowerCamelCase            =    make_list_of_images(a       )

         if not valid_images(a       ):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

         if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''       )

         if do_color_quantize and clusters is None:
             raise ValueError('''Clusters must be specified if do_color_quantize is True.'''       )

         # All transformations expect numpy arrays.
         __lowerCamelCase            =    [to_numpy_array(a       ) for image in images]

         if do_resize:
             __lowerCamelCase            =    [self.resize(image=a    ,							size=a    ,							resample=a       ) for image in images]

         if do_normalize:
             __lowerCamelCase            =    [self.normalize(image=a       ) for image in images]

         if do_color_quantize:
             __lowerCamelCase            =    [to_channel_dimension_format(a    ,							ChannelDimension.LAST       ) for image in images]
             # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
             __lowerCamelCase            =    np.array(a       )
             __lowerCamelCase            =    color_quantize(a    ,							a       ).reshape(images.shape[:-1]       )

             # flatten to (batch_size, height*width)
             __lowerCamelCase            =    images.shape[0]
             __lowerCamelCase            =    images.reshape(a    ,							-1       )

             # We need to convert back to a list of images to keep consistent behaviour across processors.
             __lowerCamelCase            =    list(a       )
         else:
             __lowerCamelCase            =    [to_channel_dimension_format(a    ,							a       ) for image in images]

         __lowerCamelCase            =    {'''input_ids''': images}
         return BatchFeature(data=a    ,							tensor_type=a       )

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       bool:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    if len(UpperCamelCase__							) == 1:
        return True
    __lowerCamelCase            =    series[1] - series[0]
    for index in range(len(UpperCamelCase__							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    __lowerCamelCase            =    0
    for val in series:
        answer += val
    return answer / len(UpperCamelCase__							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
